Minimum Time Ship Maneuvering using Neural Network and Nonlinear Model Predictive Compensator

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In this research, a new minimum-time ship maneuvering system is developed.The proposed system is mainly composed of two parts. The one is a neural network based optimal solution generator and another is a nonlinear model predictive compensator. The neural network generates the optimal solution for real situation by interpolating pre-computed minimum-time solutions for typical control conditions. The optimal solutions for the various minimum time maneuvering are numerically computed based on the sophisticated nonlinear dynamical model of the ship (MMG model) and are learned off-line by the neural network for interpolation. Moreover, the same nonlinear dynamical model, which is used for the computation of the optimal solutions, is used to simulate the ship's future course on-line. Based on the computed ship's future course, the predictive control error caused by some disturbances is compensated by modifying the control input for minimum time maneuvering.First, the solving technique of the minimum-time maneuvering problems and the mathematical model of the ship's dynamics are briefly reviewed. Next, the minimum-time parallel deviation maneuvering problem and its solutions are introduced as an example of the feasible study realized by proposed system. Then, a minimum-time maneuvering system with neural network and nonlinear model predictive compensator is introduced. Finally, computer simulations and on-line experiments are carried out for a training ship Shioji Maru (425 gross tonnage).This research presented a new practical ship's minimum-time maneuvering system with neural network and nonlinear model predictive compensator. In the minimum-time deviation problems, the system gives approximate solutions in a short computing time and good tracking performance in real situations. Moreover, the actual sea trials demonstrate the effectiveness of the proposed system.